Unlocking AI acceptance among pre-service mathematics teachers: development and validation of a TAM-based instrument - Annisa Dwi Kurniawati; Mega Melinda Ayu Pratiwi (LINEAR: Journal of Mathematics Education, 2026)
The rapid integration of artificial intelligence (AI) in mathematics education has shifted instructional practices beyond efficiency toward more transformative learning experiences. However, the successful adoption of AI depends largely on pre-service teachers' acceptance of these technologies. This study aimed to develop and validate a Technology Acceptance Model (TAM)–based instrument to measure pre-service mathematics teachers' acceptance of AI in transformative learning contexts. A research and development design employing the 4D model (Define, Design, Develop, Disseminate) was implemented. The instrument focused on four core TAM constructs: Perceived Usefulness of AI (PU-AI), Perceived Ease of Use of AI (PEOU-AI), Attitude Toward Use of AI (ATU-AI), and Behavioural Intention to Use AI (BI-AI), with items contextualised for mathematics learning and transformative pedagogical practices. Content validity was evaluated by two expert validators using the Gregory content validity coefficient, and interrater reliability was assessed to ensure consistency across expert evaluations. The results indicated high content validity (V = 0.90) and strong inter-rater agreement (80%), supporting the adequacy and clarity of the developed items. A pilot test involving 47 pre-service mathematics teachers was conducted to examine the clarity and usability of the instrument. Based on expert feedback and pilot testing, a final instrument comprising 20 items was produced, with each item representing a distinct indicator across the four constructs. Based on the results, this instrument is a potentially context-sensitive tool for assessing AI acceptance among pre-service mathematics teachers. These initial findings may inform future research and evaluation efforts, though further psychometric testing, including internal reliability, construct validity, and factor analysis, is recommended before broader application in AI-supported mathematics education.